Application of selenium isotopes to define selenium bioreduction in coal waste rock: Elk Valley, British Columbia
Bibliographic record
Abstract
Anthropogenic sources of selenium (Se), including coal mining, can release Se to the environment and raise Se concentrations in receiving waters above drinking water and aquatic limits. Selenium bioreduction can be an important control to reduce dissolved selenate concentrations. This extensive study investigated the application of Se stable isotope ratios (δ 82 Se) of dissolved selenate to identify Se bioreduction in saturated and unsaturated mine rock piles (MRPs) located in the Elk Valley, Canada. The study included in situ and laboratory column experiments, where methanol was added to promote bioreduction. Results showed that selenate concentrations often are not reliable indicators of bioreduction. However, elevation of δ 82 Se relative to the selenate sources in all environments provided a robust indicator of widespread selenate bioreduction. Native microbes were shown to use both methanol and natural carbon sources as an electron donor for selenate bioreduction. Variability in the magnitude of apparent isotopic fractionation in column experiments was attributed to physical and microbiological heterogeneity, while variability in apparent isotopic fractionation from in situ experiments was attributed to the entrainment of small masses of background selenate into the experiment. Due to the uncertainty in apparent epsilon values, the extent of bioreduction can only be estimated with limited confidence. Despite uncertainties, however, the application of δ 82 Se is valuable in guiding the management of Se in mine environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".